Phase-aberration Delay Estimation in Synthetic Transmit Aperture Diagnostic Ultrasound
Bibliographic record
Abstract
Phase aberration is one of the main contributors to image degradation in ultrasound imaging . Image reconstruction is usually performed under the assumption of a homogeneous medium. However, in the presence of the spatial sound-speed inhomogeneities, this hypothesis is no longer valid and leads to error in estimating echo arrival time. Normalized Cross Correlation (NCC) is one of the most extensively studied techniques to estimate the arrival delay error and the aberration profile. However, NCC can only estimate the relative delay errors between the probe elements and can not give the mean delay error. In this paper, an algorithm was proposed to maximize the brightness and variance over a region of interest of the reconstructed image to find the mean delay error. Firstly, conventional NCC was modified to design an iterative method that estimated the relative aberration profile in the raw synthetic transmit aperture RF signals in both transmit and receive. The phase-aberration error was corrected to yield a better focused image. Secondly, an optimization-based algorithm was developed to estimate the mean value of delay error. An absolute delay error estimator is essential for the mapping of sound-speed within the medium.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".